Neha Kokane
About Neha Kokane
Neha Kokane is a Big Data Engineer with experience in implementing data governance frameworks and developing machine learning models. She has a background in Mechanical Engineering and has contributed to optimizing data processing efficiency and mentoring junior team members.
Current Role as Big Data Engineer
Neha Kokane currently serves as a Big Data Engineer at a confidential organization in Hyderabad, Telangana, India. She has held this position since 2021. In her role, she focuses on developing scalable machine learning models and optimizing data pipelines. Her contributions have led to a notable increase in data processing efficiency and improvements in predictive analytics accuracy.
Previous Experience as Big Data Trainee
Neha Kokane worked as a Big Data Trainee at a confidential organization from 2019 to 2021. During her two-year tenure, she implemented a data governance framework that ensured compliance and security across the organization. Additionally, she mentored junior team members, promoting a culture of continuous learning within the data engineering team.
Educational Background in Mechanical Engineering
Neha Kokane obtained her Bachelor of Engineering (BE) in Mechanical Engineering from Dr. Babasaheb Ambedkar Marathwada University, Aurangabad, completing her studies from 2013 to 2017. This foundational education provided her with essential engineering principles that support her current work in data engineering.
Postgraduate Studies in CAD - CAM
Neha Kokane pursued a postgraduate diploma in CAD - CAM at Indo-German Tool Room, studying from 2019 to 2022. This program enhanced her technical skills and knowledge in computer-aided design and manufacturing, complementing her engineering background and contributing to her analytical capabilities in data engineering.
Key Contributions to Data Engineering
Throughout her career, Neha Kokane has made significant contributions to data engineering. She developed scalable machine learning models that improved predictive analytics accuracy by 15%. Additionally, she led the integration of multiple data sources, enabling real-time and historical data analysis for critical business decisions, and optimized data pipelines, resulting in a 20% increase in data processing efficiency.